Whole Genome Custom Bioinformatic Analysis

• de novo Genome Assembly
• Variant (SNP & INDEL) Discovery
• Structural Variant Discovery
• CNV Analysis
• Gene-fusion Discovery
• Exome Sequencing Analysis 
• Peak Analysis for ChIP-Seq Projects
• Annotated Reporting
• Family Trio Analysis
• Pathogenicity Scoring
• Phenotype Relationship

Whole genome bioinformatic analysis involves the computational exploration of an organism’s genetic blueprint. By leveraging advanced algorithms and computational techniques, researchers can analyze genomes to identify genes, regulatory elements, and variations across individuals or populations. This comprehensive approach enables studying complex biological processes, evolutionary relationships, and disease mechanisms on a genome-wide scale.

Epigenomics Analysis

• Methylation Profiling
• Functional Location Mapping
• Differentially Methylated CpGs
• Annotated Peak Calling
• Peak Visualization
• Motif Discovery
• Small RNA Expression Profile
• Categories of Small RNA

Epigenomics involves the profiling and analysis of epigenetic marks across the genome. Epigenetic processes in Eukaryotes generally consist of four major mechanisms: DNA methylation, histone modifications, chromatin compaction, and nuclear organization. By mapping these intricate patterns, scientists can unravel the complex mechanisms underlying gene regulation and understand how epigenetic alterations contribute to health, disease, and development. Epigenomics offers a comprehensive view of the epigenetic landscape, providing valuable insights into cellular processes, disease mechanisms, and potential therapeutic targets.

Transcriptomics (RNA-Seq) Custom Bioinformatic Analysis

• Differential Gene Expression Analysis
• Splice Variant Expression Analysis
• Novel Isoform identification
• Small RNA Expression Analysis
• de novo Transcriptome Assembly
• Variant (SNP/INDEL) Discovery
• Fusion Gene Identification
• Differential Gene Expression (DEG)
• miRNA Analysis

RNA-seq provides a methodology to quantify gene expression levels, identify alternative splicing events, detect novel transcripts, and uncover regulatory networks. This comprehensive approach provides insights into gene function, molecular pathways, and disease mechanisms. RNA-seq bioinformatic analysis is invaluable for studying gene expression dynamics, biomarker discovery, and understanding the molecular basis of various biological processes and diseases.

Metagenomics Analysis

• 16S rRNA Metagenomics Analysis
• Whole Genome Metagenomics Analysis 

Metagenomics analysis involves the study of genetic material collected directly from human and environmental samples, such as soil, water, or the human gut microbiome. ResearchDx can analyze metagenomic data through advanced bioinformatic techniques to identify and characterize microbial communities, including bacteria, viruses, fungi, and archaea. This approach allows for discovering new species, assessing community dynamics, and investigating microbial functions and metabolic pathways. Metagenomics analysis provides valuable insights into ecosystem health, biogeochemical cycles, and the role of microorganisms in human health and disease.

Proteomics Analysis

• Statistical Analyses
• Data Visualization
• Normalized Protein Expression
• Absolute Protein Quantitation

Protein bioinformatic analysis involves the computational analysis of protein sequences, structures, and functions to gain insights into their biological roles and interactions. This multidisciplinary field integrates principles from biology, computer science, and statistics to decode the complex language of proteins. ResearchDx has experience in predicting protein structure, identifying functional domains, analyzing sequence similarities, and predicting protein-protein interactions. By leveraging large-scale genomic and proteomic datasets, protein bioinformatic analysis enables researchers to uncover patterns, relationships, and potential therapeutic targets.

Pathway and Differential Expression Analysis

This analysis identifies changes in gene expression levels between different biological conditions, such as different genotypes, treated and untreated samples, or disease-related research. Differentially expressed genes provide valuable insights into studied phenotypes’ underlying drivers and affected processes.

• GO (Gene Ontology)
• KEGG (Kyoto Encyclopedia of Genes and Genomes)
• STRING

Popular requests often include::

• Enriched pathways analysis
• List of the proteins, their known functions and processes, and p-values related to their enrichment
• Molecular function analysis
• Protein interaction mapping

Bioinformatic pathway analysis involves the computational analysis of biological pathways to understand the intricate networks of interactions among genes, proteins, and other molecules. It’s like creating a roadmap of the molecular processes that govern various cellular functions and responses. RDx can work with your team to identify key pathways involved in biological phenomena or disease states by integrating data from high-throughput experiments such as genomics, transcriptomics, and proteomics and uncover regulatory mechanisms, signaling cascades, and potential therapeutic targets. Bioinformatic pathway analysis provides valuable insights into the underlying mechanisms of complex diseases, drug responses, and the interplay between biological molecules within living systems.

Targeted Panel Generation

Have a specific set of targets in mind? RDx can efficiently design and build a wide range of panel sizes, target regions, and multiplexing requirements. If you need a non-human custom panel, talk to us. We can design and build custom panels for most genomes. Additionally, our design capabilities allow us to expand or enhance content on most existing panels easily. Whether you design your own panel from scratch or add targets to enhance the content of existing panels, RDx can help.